Abstract

Systems biology is a multidisciplinary methodology in which quantitative biological experimental data are dissected using
mathematical modelling and other computational and network biology tools, aiming at understanding the structure, function
and dynamical regulation of biochemical networks. Systems biology will play a major role in the future molecular and clinical
oncology because (a) it can be used for the analysis of cancer‐relevant high‐throughput data, (b) it provides tools for the
reconstruction of the large multilevel regulatory networks that govern critical cancer phenotypes and (c) it is necessary
when investigating cancer‐relevant networks holding multiple overlapping nonlinear regulatory motifs like feedback and feedforward
loops. Here, the value of the systems biology approach in handling cancer genomics and transcriptomics data, the reconstruction
of cancer networks and the use of mathematical modelling in the elucidation of cancer networks as well as in the design of
anticancer therapies are discusses. Some recent case studies as proof of principle are highlighted.

Key Concepts:

Systems biology is a multidisciplinary approach that involves the analysis of quantitative experimental data via mathematical
modelling and computational and network biology.

Mathematical models have been used to elucidate the structure, dynamics, dysregulation and crosstalk of cancer pathways like
the JAK/STAT, p53, MAPK, NFkB and intrinsinc/extrinsic apoptosis signalling pathways, as well as in microRNA cancer regulation.

Multi‐scale mathematical models can accurately describe the spatial configuration of tumours, their crosstalk with the surrounding
microenvironment and their role in physiological mechanisms like angiogenesis and immune system response.

Mathematical modelling has been used to assess and personalise conventional anticancer therapies, but it is also used in the
detection of new anticancer drug targets and the development of combined or immune therapies.

Cancer bioinformatics facilitates efficient analyses and integration of diverse types of biomedical high‐throughput data for
the elucidation of causes of tumour initiation, progression and metastasis but also for the management, storage and exchange
of experimental and clinical data.

Sketch of the standard workflow followed for mathematical modelling in cancer systems biology.

Figure 2.

Depicted is an exemplary workflow in cancer bioinformatics. It illustrates that the integration of a wide range of approaches and resources is necessary to better understand the molecular mechanisms underlying the complex disease of cancer as well as for the diagnosis, prognosis and the design of personalised therapies.

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